How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how a post’s features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a post in each round so as to maximize the cumulative influence of those posts over all rounds. We solve ACAIM by applying, for the first time, an Online Learning to Rank (OLR) framework for IM purposes. We introduce the CATRID propagation model, which expresses how posts disseminate in a social network using click probabilities and post visibility criteria and develop a simulator that runs CATRID via a training-testing scheme based on real posts of the VK social network, so as to realistically represent the learning environment. We deploy three learners that solve ACAIM in an online (real-time) manner. We experimentally prove the practical suitability of our solutions via exhaustive experiments on multiple brands (operating as different case studies) and several VK datasets; the best learner is evaluated on 45 separate case studies yielding convincing results.

我们如何才能在一系列轮次中调整帖子的构成，使其在社交网络中更具吸引力？在影响力最大化（IM）问题的背景下研究了如何逐步学习如何使固定帖子在各轮中更具影响力的技术，该问题寻求一组种子用户来最大化帖子的影响力。然而，目前还没有关于逐步了解帖子的特征如何影响其影响力的工作。在本文中，我们提出并研究了自适应内容感知影响力最大化（ACAIM）问题，该问题要求在每一轮中找到k 个特征来形成帖子，从而最大化这些帖子在所有轮次中的累积影响力。我们首次通过应用在线学习排序(OLR) 框架来解决 ACAIM 问题。我们引入了 CATRID传播模型，该模型使用点击概率和帖子可见性标准来表达帖子如何在社交网络中传播，并开发了一个模拟器，通过基于 VK 社交网络真实帖子的训练测试方案来运行 CATRID，以便现实地代表学习环境。我们部署了三个学习者以在线（实时）方式解决 ACAIM。我们通过对多个品牌（作为不同案例研究进行操作）和多个 VK 数据集的详尽实验来证明我们的解决方案的实际适用性；最佳学习者通过 45 个单独的案例研究进行评估，得出令人信服的结果。